ABSTRACT
The outbreak of COVID-19 has caused an exponential increase in mortality rate globally and has dealt a devastating blow to nations all over the world. This unforeseen calamity needs to be tackled and early detection of this disease could help in this regard. Several research studies used Chest X-rays and CT scans to detect the disease, which can be made cost-effective by using cough samples. These systems can further be refined by using multiple health parameters to provide more accurate results. In this view, this paper proposes a constructive way for the early detection of COVID-19 by considering cough samples and clinical data (Saturation of Peripheral Oxygen (SpO2) level, body temperature, heart rate, and symptoms). The dataset was collected by using a Raspberry Pi and an online questionnaire. In this paper, we put forward two approaches being Manual feature extraction and Mixed data neural networks (Multi-layer Perceptron and Convolutional Neural Networks) for efficiently handling the problem. To help the user access the system more comfortably, a mobile application was developed. The Mixed data neural networks yielded the best performance with an Area Under the Curve (AUC) score of 0.94 and an accuracy of 0.85. © 2021 IEEE.